Paper Title
Loan Eligibility Prediction System
Article Identifiers
: Keywords
Loan eligibility prediction, machine learning
Abstract
In the modern financial system, banks give firms or people looking to buy anything the necessary initial investment. to assess a borrower’s creditworthiness and forecast the possibility that they will be granted a loan. For lenders, banks, and financial organisations, a loan eligibility prediction system can be helpful in automating the loan application process and determining the risk of giving money to a certain applicant. It is a piece of software that uses techniques for data analysis and machine learning. The system includes compiling data on sanctioned loans and loan applications from a variety of sources. The data contains facts on the borrower’s income, job history, debt-to-income ratio, loan amount, loan period, and other relevant information. The data is then prepared for use in the machine’s training by being cleaned, preprocessed, and transformed. Then, relevant traits that can influence loan eligibility are identified from the data. This entails creating new factors or changing the ones already in use to predict loan eligibility. Following the division of the data into train and test sets, a machine learning model is selected and trained from different algorithms that are available. The testing set is used to evaluate the model’s performance after it has been trained on the training set. After the method for predicting loan eligibility is created, it can be incorporated into a programme that banks and lenders can use to determine loan eligibility. The loan eligibility decisionmaking process should be well explained in the application, which should also be easy to use. To make sure the model is reliable and useful over time, the loan eligibility prediction system should be constantly reviewed and updated with fresh data. In conclusion, a loan eligibility prediction system will be a useful tool for banks, financial institutions, and lenders to automate the application process and determine the risk involved in giving money to a certain borrower. The system entails gathering, pre-processing, and manipulating data; extracting pertinent features; choosing an appropriate machine learning model; training the model; and implementing it in a lending and banking application.
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How To Cite (APA)
Chandan Singh Palhania, Amit Kumar Jaiswal, Gaurav Kumar, Utkarsh Raj, & Shekhar Rana (April-2023). Loan Eligibility Prediction System. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(4), d201-d205. https://ijnrd.org/papers/IJNRD2304328.pdf
Issue
Volume 8 Issue 4, April-2023
Pages : d201-d205
Other Publication Details
Paper Reg. ID: IJNRD_191320
Published Paper Id: IJNRD2304328
Downloads: 000122254
Research Area: Engineering
Author Type: Indian Author
Country: MOHALI, Panjab , India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2304328.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2304328
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